Common image corruptions.
[1]} first find that despite being on par with the human vision on standard images, deep networks perform suboptimally on common corruptions such as noise and blur.
[2]} measure the performance of deep networks on 12 different image corruption types but find that data augmentation on one type of corruption does not tend to improve the performance on others.
However, these findings are reconsidered in [3]} where Gaussian data augmentation is shown to help for a wide range of image corruptions.
In a standardization effort, [4]} introduce a few image classification datasets—in particular, CIFAR-10-C and ImageNet-C—with 15 different common corruptions from four categories: noise, blur, weather, and digital corruptions.
[5]} show that not only acccuracy but also calibration deteriorates under these common corruptions.
[6]}, [7]} show that robustness to common corruptions can be improved by using test-time adaptation, e.g., via recomputing the batch normalization statistics.
[8]} show that contrastive pretraining on a very large set of image-caption pairs can substantially improve robustness on various distribution shifts including common corruptions.
